If you haven’t already, check out Arun Ulag’s hero blog “Microsoft Build 2026: Building Agentic Apps with Microsoft Fabric and Microsoft Databases” for a complete look at all of our Microsoft Build announcements across our Fabric and database offerings.
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Organizations are shifting toward relationship-first data platforms, where context is explicitly modeled rather than inferred. With graph now Generally Available, we’re taking a major step in enabling that shift.
AI systems today aren’t limited by how much data they can access; they’re limited by how well they understand how that data is connected. Most enterprise data platforms still reason over isolated tables, but business decisions are inherently interwoven: customers connected to purchases, suppliers connected to products, identities connected to devices. Fabric IQ unifies business semantics across data, models, and systems to power intelligent agents and decisions grounded in a live, holistic view of the business.
What is Graph in Fabric?
Graph in Fabric helps you model, visualize, and analyze complex relationships within your data. It's a scalable, enterprise-grade solution that turns disconnected data into AI-powered insights. By using Graph, you can uncover hidden connections within your data and enhance decision-making capabilities.
Graph also enables agents to reason at enterprise scale. AI reasoning over complex data environments depends on paths, dependencies, and cascading impact across entities, capabilities that are expensive or impractical to express through joins alone. Graph makes those relationships explicit, allowing data agents in Fabric to execute deterministic queries over connected business context rather than infer structure probabilistically.
You can further leverage the power of graphs in Fabric Ontology, which defines shared business meaning with vocabulary, entities, rules, and constraints. This additional layer of business context allows organizations to implement knowledge graphs that are both semantically aligned and operationally performant, enabling AI agents to perform domain-adapted reasoning consistently.
Our customer, Truman Seto, Manager, Operations Planning at ENMAX Power Corporation shared:
Microsoft Fabric IQ provides an incredible opportunity for domain experts to create and analyze strategic ontologies that prioritize complex data and business relationships. Companies are overwhelmed with the amount of static and time series data. To “connect the dots” a graph-based ontology is critical to map the core data interdependencies in business-friendly language. Fabric IQ provides a platform to make sense of your data and drive knowledge-based action. This has been especially impressive in documenting and analyzing grid operations in an interconnected transmission and distribution power system.
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Figure: Building a network with Graph in Fabric.
Key capabilities at a glance
- Native OneLake Integration:Operates directly on your existing data in OneLake—avoid managing complex data movement and ETL.
- Scale-Out Architecture: Distributed, sharded processing for large-scale graphs with billions of relationships.
- GQL Standard Support: Standards-based querying (ISO/IEC 39075) with pattern matching, path traversal, and aggregations—portable across GQL-compliant systems.
- NL2GQL (Natural Language to Graph Query Language): Ask questions about your data in natural language, lowering the barrier for non-technical users.
- Role-Based Experiences: Business users explore visually; analysts run low/no-code queries; engineers model graphs; scientists apply algorithms; developers build AI agents.
- Unified Security & Governance: Inherits Fabric's OneLake governance, RBAC, lineage tracking, and compliance standards—one security model for all data.
Decision scenarios
Graph unlocks powerful use cases across industries wherever connected data matters:
- Supply chain and logistics: Visualize and optimize networks of suppliers, warehouses, distribution centers, and retailers to identify bottlenecks and improve efficiency.
- Fraud investigation (banking and insurance): Starting from a flagged account, investigators need to trace how funds move across intermediary accounts to downstream institutions, identifying mule networks that would be missed when transactions are analyzed in isolation
- Customer intelligence and recommendations (e-commerce): Map customer purchase history, browsing behavior, and product relationships to power personalized recommendations and deeper customer understanding.
- Ontology and AI applications: Serve as the relational backbone for enterprise ontologies and AI agents, Graph powers Fabric IQ Ontology for cross-domain reasoning over business entities, rules, and relationships.
Graph in cybersecurity
Security systems are inherently relationship-heavy as identities connect to tenants, tenants to policies, and policies to services. This makes impact analysis difficult to express with traditional analytics. Microsoft Sentinel Graph uses graph to model these dependencies explicitly and reason over hundreds of millions of connected entities. Instead of relying on complex and fragile joins, they use native graph traversals to trace blast radius, understand downstream impact, and evaluate security posture across multiple hops. As graph becomes generally available, its performance and reliability are already proven by its ability to support mission‑critical scenarios.
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Figure: Analyzing relationships in Graph in Fabric
Get started
Explore Graph in Fabric today with the following resources:
- Complete a tutorial: Real-Time Intelligence | Ontology
- Ask questions on the forum: Real-Time Intelligence | IQ
- Submit ideas and vote: Real-Time Intelligence | IQ
- Use the docs: Real-Time Intelligence | IQ
- Complete the learning path: Real-Time Intelligence
- Get certified: Real-Time Intelligence
- Read the blog: Real-Time Intelligence | IQ
- Check the release plan: Real-Time Intelligence | IQ
- Recent Webinar: Connect Your Data with Graph in Microsoft Fabric: Why Relationships Matter for AI | Microsoft Reactor
Within Fabric IQ, Graph serves as the execution layer for relationship aware reasoning— enabling semantic models, ontologies, and agents to operate over the same connected business context. Graph isn’t a trend, it’s foundational infrastructure for the most connected, AI‑driven systems we’re building now.
Microsoft Employee